Should Your Voice Determine Whether You Get Hired?

Technology is changing every facet of work, including how companies profile and select their employees. The development of different apps, software, and algorithms has produced many novel methodologies for screening job candidates and evaluating their potential fit for a role or organization.

The latest of such methods is voice profiling, the use of computer-based algorithms to predict job fit based on an analysis of a candidate’s voice. According to news reports, “regardless of whether you’re happy, sad, or cracking jokes, your voice has a hidden, complicated architecture with an intrinsic signature—much like a fingerprint. Through trial and error, the algorithms can get better at predicting how things like energy and fundamental frequency impact others—be they people watching a movie, or cancer patients calling a help line.”

Although the idea that each voice is unique makes intuitive sense, some voice profiling tools, such as Jobaline, are based on a rather unconventional premise: Instead of trying to decode a candidate’s personality, intelligence, or mood state, they aim to predict “the emotion that that voice is going to generate on the listener.” In other words, the algorithm functions as a mechanical judge in a voice-based beauty contest. Desirable voices are invited to the next round, where they are judged by humans, while undesirable voices are eliminated from the contest.

As with many other innovations in the space of HR technologies, evaluating claims about the accuracy of this method is difficult until independent academic research has been conducted. The good news, however, is that there is a well-established formula for testing whether the method works: (1) measure features of candidates’ voices, (2) measure listeners’ reactions, and (3) measure whether listeners’ reactions relate to positive organizational outcomes, such as revenues, profits, and customer satisfaction. Then, correlate 1 and 2, as well as 2 and 3. If a pattern is found, voice profiling can be deemed an effective talent signal for identifying, and hopefully selecting, future top performers.

So far, efforts to validate this methodology appear to have focused only on the relationship between 1 and 2. That is not enough. For voice profiling to have any value in making hiring decisions, the emotional reactions must demonstrably cause higher levels of performance. In other words, even if technology can be used to quantify the physical properties that make some voices more appealing than others, the big question is whether the measured properties truly contribute to any desirable organizational outcomes, or whether they simply advance an individual’s career because it makes him or her more likable.

In addition, there are several as-yet-unanswered questions that should be addressed:

How consistent is a person’s voice profile? Is someone’s profile the same when whispering sweet nothings to a loved one and when making a high-stakes presentation to a panel of senior execs? Probably not. Which, then, is the “true” profile?

How uniform are the emotional reactions to a particular voice pattern, particularly among a diverse audience? One of the best-documented psychological phenomena is the wide range of emotional responses that observers have to the same behavioral stimulus. Think of the voice profiles of a singer. Don’t the feelings evoked by the voices of, say, Bob Dylan and Björk vary wildly? If one voice can generate such varied reactions, what is the precise reaction we are trying to predict? And if there is a wide range of variability in people’s reactions, is the average reaction meaningful?

How meaningful are the patterns identified? Technology can slice and dice speech in literally hundreds of ways—there are multiple dimensions of tone and pitch, of cadence and pauses. One company analyzes video and voice recordings of 15-minute job interviews to measure over 1,000 characteristics of speech for each candidate. When all of this slicing and dicing generates so many variables, which are then correlated with job performance, inevitably some variables will be statistically significant but nonetheless make little conceptual sense. That is, we will have gained no real understanding of why a particular candidate is or isn’t a good fit for the job, and therefore we can do little to improve our ability to actually boost performance. All we will have is an enigmatic algorithm that somehow works because we put so much information into it.

Do these questions mean voice profiling is a dead end? Not at all. But before we can get excited about this technological innovation, we need to:

Start with credible hypotheses about exactly how an employee’s voice quality or speech affects job performance in a target role.

Identify the key voice attributes and how they affect the behavior of other people (e.g., colleagues, subordinates, and customers).

Standardize testing conditions, perhaps even creating a job-relevant role-play exercise that all candidates go through to capture voice attributes in a reliable manner.

Focus on how voice tone affects listeners’ behavior, not just the emotions they feel.

Finally, we should think carefully about the ethical implications of adopting this method. Even if its accuracy can be demonstrated, do we really want to reject a job candidate because of a physiologically determined, largely unchangeable feature? Height, physical attractiveness, and other similar characteristics have long been positively linked to job performance and career success, but it is surely unfair to select candidates on the basis of attributes they cannot control.

Voice profiling may represent a promising start in harnessing technology to spot great hires—but it is just a start, with many questions still to be answered. Until we are able to understand precisely how a person’s voice may contribute to organizational effectiveness, and whether it does so better than alternative attributes that a candidate can control, we should regard voice profiling only as an interesting experiment.

Seymour Adler, Ph.D., is Professor of Industrial/Organizational Psychology at Hofstra University and a Partner in the Performance, Talent, and Rewards practice at Aon Hewitt. He is the coauthor of Technology-Enhanced Assessment of Talent (Jossey-Bass, 2011) as well many academic and professional publications.